7 مستودعات
APIs for executing SQL queries within application code.
Distinguishing note: Focuses on Python-based SQL execution.
Explore 7 awesome GitHub repositories matching data & databases · SQL Execution Interfaces. Refine with filters or upvote what's useful.
DuckDB is an in-process analytical database engine designed to run directly within an application process. As a zero-dependency, embedded system, it provides enterprise-grade SQL data processing capabilities without the overhead of managing a dedicated database server. It is built to handle complex analytical and aggregation tasks by storing and retrieving information in columns, allowing for high-performance relational data manipulation. The engine distinguishes itself through a columnar vectorized execution model that maximizes CPU cache efficiency during query operations. It employs adapti
Allows Python applications to run SQL commands against in-memory or persistent storage.
Moto is a cloud service mockery framework and API mock server that simulates AWS infrastructure locally. It allows developers to test cloud-dependent code and verify infrastructure-as-code templates without deploying real resources or incurring costs. The project functions as an SDK interceptor that can patch existing service clients to redirect requests to a local mock environment. It can also be run as a standalone HTTP server, enabling any programming language to interact with the simulated endpoints. The framework covers a vast array of simulated capabilities, including data storage, com
Simulates the execution of SQL queries against relational database mocks via API.
Squirrel is a Go database library and SQL query builder that provides a programmatic interface for constructing and executing SQL statements. It enables the creation of queries using a fluent interface to avoid manual string concatenation. The library functions as a SQL dialect generator, producing queries formatted for specific database engines by adjusting placeholders and syntax to match target requirements. This allows for the generation of SQL compatible with multiple different database environments. Beyond query construction, the project covers the execution of generated statements aga
Wraps database driver interfaces to provide a unified execution method for generated queries.
Apache Hive is a SQL-on-Hadoop data warehouse that enables querying and managing petabytes of data stored in distributed storage such as HDFS and cloud storage services. It provides a familiar SQL interface for batch analytics and reporting, supported by a core set of components including the HiveServer2 Thrift service for remote query execution, the Hive Metastore Service for central metadata management, the Hive ACID Transaction Engine for concurrent read-write operations, and the Hive LLAP Interactive Engine for low-latency analytical processing. The WebHCat REST API offers an HTTP interfac
Executes procedural SQL code, including PL/SQL and T-SQL, on Hive and other engines.
AliSQL is a fork of MySQL by Alibaba that extends the relational database management system with enhancements for high performance, scalability, and enterprise-grade availability. It retains the core MySQL identity as a SQL-based database for storing, organizing, and retrieving structured data, while adding optimizations for large-scale transactional and analytical workloads. The project differentiates itself through a set of Alibaba-specific improvements, including a columnar engine for accelerating analytical queries directly on MySQL tables, and a distributed, shared-nothing NDB Cluster en
Provides a C++ interface to run SQL statements against a MySQL database and retrieve result sets.
Rusqlite is an embedded database interface and relational database driver that provides a client library for interacting with SQLite. It functions as an SQL query wrapper, enabling the management of local file-based or in-memory databases through a safe interface. The library allows for the extension of native database capabilities by implementing custom scalar functions, collations, and virtual tables. It also supports the embedding of the database engine directly into the application binary to remove external library dependencies. The project covers a broad range of capabilities including
Executes database commands with custom parameters to modify data and retrieve affected row counts.
Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across diverse data sources and cloud storage. It serves as a centralized interface for governing schemas, access controls, and tagging across relational databases, messaging queues, and object stores. The project distinguishes itself by unifying the management of AI assets, such as machine learning models and their version lineages, alongside traditional tabular data. It also implements the Iceberg REST specification to provide a standardized metadata server and proxy for lakehouse
Performs standard data definition and manipulation operations across federated data assets using SQL.